Abstract
In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 |
| Publisher | IEEE Computer Society |
| Pages | 4303-4311 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781467388504 |
| DOIs | |
| Publication status | Published - 2016 Dec 9 |
| Event | 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States Duration: 2016 Jun 26 → 2016 Jul 1 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| Volume | 2016-December |
| ISSN (Print) | 1063-6919 |
Conference
| Conference | 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 16/6/26 → 16/7/1 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
Cite this
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Hedged Deep Tracking. / Qi, Yuankai; Zhang, Shengping; Qin, Lei; Yao, Hongxun; Huang, Qingming; Lim, Jongwoo; Yang, Ming Hsuan.
Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. IEEE Computer Society, 2016. p. 4303-4311 7780835 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Hedged Deep Tracking
AU - Qi, Yuankai
AU - Zhang, Shengping
AU - Qin, Lei
AU - Yao, Hongxun
AU - Huang, Qingming
AU - Lim, Jongwoo
AU - Yang, Ming Hsuan
PY - 2016/12/9
Y1 - 2016/12/9
N2 - In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.
AB - In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.
UR - http://www.scopus.com/inward/record.url?scp=84986246054&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986246054&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2016.466
DO - 10.1109/CVPR.2016.466
M3 - Conference contribution
AN - SCOPUS:84986246054
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 4303
EP - 4311
BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PB - IEEE Computer Society
ER -